## # A tibble: 6 × 53
## STATE ST_CASE VE_TOTAL VE_FORMS PVH_INVL PEDS PERNOTMVIT PERMVIT
## <int> <int> <int> <int> <int> <int> <int> <int>
## 1 1 10001 1 1 0 0 0 1
## 2 1 10002 1 1 0 0 0 1
## 3 1 10003 1 1 0 0 0 2
## 4 1 10004 1 1 0 0 0 1
## 5 1 10005 2 2 0 0 0 2
## 6 1 10006 1 1 0 0 0 2
## # ... with 45 more variables: PERSONS <int>, COUNTY <int>, CITY <int>,
## # DAY <int>, MONTH <int>, YEAR <int>, DAY_WEEK <int>, HOUR <int>,
## # MINUTE <int>, NHS <int>, RUR_URB <int>, FUNC_SYS <int>,
## # RD_OWNER <int>, ROUTE <int>, TWAY_ID <chr>, TWAY_ID2 <chr>,
## # MILEPT <int>, LATITUDE <dbl>, LONGITUD <dbl>, SP_JUR <int>,
## # HARM_EV <int>, MAN_COLL <int>, RELJCT1 <int>, RELJCT2 <int>,
## # TYP_INT <int>, WRK_ZONE <int>, REL_ROAD <int>, LGT_COND <int>,
## # WEATHER1 <int>, WEATHER2 <int>, WEATHER <int>, SCH_BUS <int>,
## # RAIL <chr>, NOT_HOUR <int>, NOT_MIN <int>, ARR_HOUR <int>,
## # ARR_MIN <int>, HOSP_HR <int>, HOSP_MN <int>, CF1 <int>, CF2 <int>,
## # CF3 <int>, FATALS <int>, DRUNK_DR <int>, DRUNK <lgl>
## # A tibble: 6 × 102
## STATE ST_CASE VEH_NO VE_FORMS NUMOCCS DAY MONTH HOUR MINUTE HARM_EV
## <int> <int> <int> <int> <int> <int> <int> <int> <int> <int>
## 1 1 10001 1 1 1 1 1 2 40 35
## 2 1 10002 1 1 1 1 1 22 13 34
## 3 1 10003 1 1 2 1 1 1 25 42
## 4 1 10004 1 1 1 4 1 0 57 53
## 5 1 10005 1 2 1 7 1 7 9 12
## 6 1 10005 2 2 1 7 1 7 9 12
## # ... with 92 more variables: MAN_COLL <int>, UNITTYPE <int>,
## # HIT_RUN <int>, REG_STAT <int>, OWNER <int>, MAKE <int>, MODEL <int>,
## # MAK_MOD <int>, BODY_TYP <int>, MOD_YEAR <int>, VIN <chr>, VIN_1 <chr>,
## # VIN_2 <chr>, VIN_3 <chr>, VIN_4 <chr>, VIN_5 <chr>, VIN_6 <chr>,
## # VIN_7 <chr>, VIN_8 <chr>, VIN_9 <chr>, VIN_10 <chr>, VIN_11 <chr>,
## # VIN_12 <chr>, TOW_VEH <int>, J_KNIFE <int>, MCARR_I1 <int>,
## # MCARR_I2 <chr>, MCARR_ID <chr>, GVWR <int>, V_CONFIG <int>,
## # CARGO_BT <int>, HAZ_INV <int>, HAZ_PLAC <int>, HAZ_ID <int>,
## # HAZ_CNO <int>, HAZ_REL <int>, BUS_USE <int>, SPEC_USE <int>,
## # EMER_USE <int>, TRAV_SP <int>, UNDERIDE <int>, ROLLOVER <int>,
## # ROLINLOC <int>, IMPACT1 <int>, DEFORMED <int>, TOWED <int>,
## # M_HARM <int>, VEH_SC1 <int>, VEH_SC2 <int>, FIRE_EXP <int>,
## # DR_PRES <int>, L_STATE <int>, DR_ZIP <int>, L_STATUS <int>,
## # L_TYPE <int>, CDL_STAT <int>, L_ENDORS <int>, L_COMPL <int>,
## # L_RESTRI <int>, DR_HGT <int>, DR_WGT <int>, PREV_ACC <int>,
## # PREV_SUS <int>, PREV_DWI <int>, PREV_SPD <int>, PREV_OTH <int>,
## # FIRST_MO <int>, FIRST_YR <int>, LAST_MO <int>, LAST_YR <int>,
## # SPEEDREL <int>, DR_SF1 <int>, DR_SF2 <int>, DR_SF3 <int>,
## # DR_SF4 <int>, VTRAFWAY <int>, VNUM_LAN <int>, VSPD_LIM <int>,
## # VALIGN <int>, VPROFILE <int>, VPAVETYP <int>, VSURCOND <int>,
## # VTRAFCON <int>, VTCONT_F <int>, P_CRASH1 <int>, P_CRASH2 <int>,
## # P_CRASH3 <int>, PCRASH4 <int>, PCRASH5 <int>, ACC_TYPE <int>,
## # DEATHS <int>, DR_DRINK <int>
1-3 vehicles involved is so dominat that we cant even see that there are some rare large pileups ranging all the way to 58 cars involved!
##
## 1 2 3 4 5 6 7 8 9 10 11 12
## 18070 11616 1815 418 119 61 30 14 10 1 2 2
## 14 16 19 22 29 31 58
## 1 1 2 1 1 1 1
Alright the data looks like a smashed map, and if we check the data guide we see:
| LONGITUD | Meaning |
|---|---|
| DDD.DDDD | Actual Degrees |
| 777.7777 | Not Reported |
| 888.8888 | Not Available (If State Exempt) |
| 999.9999 | Unknown |
So we can drop any longitude greater than 0 since valid US locations should be negative, then we are ready to plot.
As heatmaps usually seem to turn out this is just a population map. This kind of validates that traffic fatalities happen where people live… which isn’t exactly shocking.
## [1] "STATE" "ST_CASE" "VE_FORMS" "VEH_NO" "PER_NO"
## [6] "STR_VEH" "COUNTY" "DAY" "MONTH" "HOUR"
## [11] "MINUTE" "RUR_URB" "FUNC_SYS" "HARM_EV" "MAN_COLL"
## [16] "SCH_BUS" "MAKE" "MAK_MOD" "BODY_TYP" "MOD_YEAR"
## [21] "TOW_VEH" "SPEC_USE" "EMER_USE" "ROLLOVER" "IMPACT1"
## [26] "FIRE_EXP" "AGE" "SEX" "PER_TYP" "INJ_SEV"
## [31] "SEAT_POS" "REST_USE" "REST_MIS" "AIR_BAG" "EJECTION"
## [36] "EJ_PATH" "EXTRICAT" "DRINKING" "ALC_DET" "ALC_STATUS"
## [41] "ATST_TYP" "ALC_RES" "DRUGS" "DRUG_DET" "DSTATUS"
## [46] "DRUGTST1" "DRUGTST2" "DRUGTST3" "DRUGRES1" "DRUGRES2"
## [51] "DRUGRES3" "HOSPITAL" "DOA" "DEATH_DA" "DEATH_MO"
## [56] "DEATH_YR" "DEATH_HR" "DEATH_MN" "DEATH_TM" "LAG_HRS"
## [61] "LAG_MINS" "P_SF1" "P_SF2" "P_SF3" "WORK_INJ"
## [66] "HISPANIC" "RACE" "LOCATION" "SURVIVED"
Maps
Person Data: Age (possible to compair to age distribution of state?) Compair types of restraints and injury severity Drinking vs. Age Drugs vs. age Underage drunks Drugs by state Breakdowns of cycalist info Lag time from crash to death How offten are they at work * Is there something interesting here when combined with time of day? LOCATION for where non-motorists were durrint time of crash
## [1] 25